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Blog

We are pleased to announce the May 2020 data update of the Cambridge Structural Database (CSD) is now available! This data update brings you 10,188 new structures (10,697 new entries) and increases the total size of the CSD to over 1,048,000 structures (1,067,000 entries).

In recent years, we have noticed an increase in the number of structures deposited to the Cambridge Structural Database (CSD) that are measured with electron diffraction techniques. As of the beginning of 2020, approximately 50 electron structures have been added to the CSD. Since this field of research is rapidly developing, we thought it timely to investigate all the electron studies in the database to ensure they can be easily located and have worked to identify any structures that were missed during the initial data curation process.

One of the major developments in the 2020.1 CSD Releaseis the addition of the CSD Pipeline Pilot component collection, which will allow you to build custom tools for analysing CSD structural data without writing code.

As well as allowing research to be done faster and more efficiently, this should remove barriers to entry and allow more people to create custom analyses.

Machine learning is a fast growing area of active research within structural science and it is particularly effective in the crystallographic structural sciences due to the wealth of highly accurate structural data available. A key part of machine learning though is having effective molecular descriptors to represent complex chemical information about molecules and structures into easily machine-interpretable vectors of numbers to feed into machine learning algorithms.

We live in exciting times for Artificial Intelligence (AI) - with the rise of new and easy to implement Machine Learning (ML) algorithms. Many of us would sooner trust a GPS to take us from point A to point B than consult a map ourselves, and robots are already being used to perform medical procedures. But what do all of these advanced techniques and algorithms mean for us as scientists and how can we use them to advance science? Presumably, many would ask if AI approaches can help, or even replace scientific experiments?

A few months ago, watching the news of COVID-19 spreading, we knew it would not be safe to hold our user group meeting at a hotel in Cambridge, MA as planned. Rather than cancelling, we moved this to become a virtual event which went ahead on the same date, 24th April 2020.

The way that we use version numbers in our software applications is changing - and this should now be much simpler! This blog explains what's changed, and how you can make sure you have the most up to date version.

In recent years, the Njarðarson research group at the University of Arizona have created posters of the top 200 drugs by sales for education, research purposes and scientific communication.1 When the latest poster based on 2018 data became available for download on their website, I was interested in finding out how many of the top-selling drugs have crystal structures in the Cambridge Structural Database (CSD).2